Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations57770
Missing cells167150
Missing cells (%)13.8%
Duplicate rows27884
Duplicate rows (%)48.3%
Total size in memory9.4 MiB
Average record size in memory171.1 B

Variable types

Numeric12
Categorical8
DateTime1

Alerts

Dataset has 27884 (48.3%) duplicate rowsDuplicates
SellerG has a high cardinality: 363 distinct values High cardinality
Postcode has a high cardinality: 208 distinct values High cardinality
Address has a high cardinality: 27304 distinct values High cardinality
Suburb has a high cardinality: 347 distinct values High cardinality
Bathroom is highly overall correlated with Bedroom2 and 2 other fieldsHigh correlation
Bedroom2 is highly overall correlated with Bathroom and 2 other fieldsHigh correlation
BuildingArea is highly overall correlated with Bathroom and 3 other fieldsHigh correlation
CouncilArea is highly overall correlated with Distance and 3 other fieldsHigh correlation
Distance is highly overall correlated with CouncilAreaHigh correlation
Lattitude is highly overall correlated with CouncilAreaHigh correlation
Longtitude is highly overall correlated with CouncilArea and 1 other fieldsHigh correlation
Price is highly overall correlated with BuildingArea and 1 other fieldsHigh correlation
Regionname is highly overall correlated with CouncilArea and 1 other fieldsHigh correlation
Rooms is highly overall correlated with Bathroom and 3 other fieldsHigh correlation
Longtitude has 13208 (22.9%) missing values Missing
BuildingArea has 34904 (60.4%) missing values Missing
Landsize has 19563 (33.9%) missing values Missing
Price has 12581 (21.8%) missing values Missing
Bathroom has 13627 (23.6%) missing values Missing
Car has 14477 (25.1%) missing values Missing
Bedroom2 has 13615 (23.6%) missing values Missing
Lattitude has 13208 (22.9%) missing values Missing
YearBuilt has 31927 (55.3%) missing values Missing
BuildingArea is highly skewed (γ1 = 23.97029648) Skewed
Landsize is highly skewed (γ1 = 90.36563886) Skewed
Address is uniformly distributed Uniform
Landsize has 4021 (7.0%) zeros Zeros
Car has 2765 (4.8%) zeros Zeros

Reproduction

Analysis started2025-02-27 05:04:15.251948
Analysis finished2025-02-27 05:04:39.148779
Duration23.9 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Longtitude
Real number (ℝ)

High correlation  Missing 

Distinct12436
Distinct (%)27.9%
Missing13208
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean145.00123
Minimum144.43162
Maximum145.52635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size451.5 KiB
2025-02-27T05:04:39.244687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum144.43162
5-th percentile144.79764
Q1144.9325
median145.0071
Q3145.07167
95-th percentile145.18737
Maximum145.52635
Range1.09473
Interquartile range (IQR)0.13917

Descriptive statistics

Standard deviation0.12049394
Coefficient of variation (CV)0.00083098563
Kurtosis1.4972509
Mean145.00123
Median Absolute Deviation (MAD)0.0686
Skewness-0.38359525
Sum6461545
Variance0.01451879
MonotonicityNot monotonic
2025-02-27T05:04:39.395357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145.0001 33
 
0.1%
144.9966 32
 
0.1%
145.0104 32
 
0.1%
144.9679 31
 
0.1%
144.985 29
 
0.1%
145.0545 29
 
0.1%
145.0118 29
 
0.1%
144.9911 29
 
0.1%
145.0451 27
 
< 0.1%
144.9974 27
 
< 0.1%
Other values (12426) 44264
76.6%
(Missing) 13208
 
22.9%
ValueCountFrequency (%)
144.43162 2
< 0.1%
144.43181 2
< 0.1%
144.4394 2
< 0.1%
144.49 2
< 0.1%
144.4926 2
< 0.1%
144.513 2
< 0.1%
144.5206 2
< 0.1%
144.53828 2
< 0.1%
144.54022 2
< 0.1%
144.54237 2
< 0.1%
ValueCountFrequency (%)
145.52635 2
< 0.1%
145.5237 2
< 0.1%
145.51137 2
< 0.1%
145.48273 2
< 0.1%
145.48246 2
< 0.1%
145.4779 3
< 0.1%
145.47282 2
< 0.1%
145.47262 2
< 0.1%
145.47052 2
< 0.1%
145.46784 2
< 0.1%

BuildingArea
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct672
Distinct (%)2.9%
Missing34904
Missing (%)60.4%
Infinite0
Infinite (%)0.0%
Mean157.49902
Minimum0
Maximum6791
Zeros136
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size451.5 KiB
2025-02-27T05:04:39.569819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile56
Q1102
median136
Q3188
95-th percentile309
Maximum6791
Range6791
Interquartile range (IQR)86

Descriptive statistics

Standard deviation146.18108
Coefficient of variation (CV)0.92813963
Kurtosis908.077
Mean157.49902
Median Absolute Deviation (MAD)40
Skewness23.970296
Sum3601372.5
Variance21368.908
MonotonicityNot monotonic
2025-02-27T05:04:39.717748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 293
 
0.5%
110 275
 
0.5%
100 274
 
0.5%
130 261
 
0.5%
115 241
 
0.4%
140 226
 
0.4%
160 217
 
0.4%
150 217
 
0.4%
125 200
 
0.3%
112 195
 
0.3%
Other values (662) 20467
35.4%
(Missing) 34904
60.4%
ValueCountFrequency (%)
0 136
0.2%
0.01 2
 
< 0.1%
1 24
 
< 0.1%
2 37
 
0.1%
3 42
 
0.1%
4 8
 
< 0.1%
5 6
 
< 0.1%
9 3
 
< 0.1%
11 2
 
< 0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
6791 2
< 0.1%
6178 3
< 0.1%
4645 2
< 0.1%
3647 3
< 0.1%
3558 2
< 0.1%
3112 2
< 0.1%
1143 2
< 0.1%
1044 2
< 0.1%
1022 2
< 0.1%
999 2
< 0.1%

Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size56.8 KiB
h
39806 
u
12062 
t
5900 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters57768
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowh
2nd rowh
3rd rowh
4th rowu
5th rowu

Common Values

ValueCountFrequency (%)
h 39806
68.9%
u 12062
 
20.9%
t 5900
 
10.2%
(Missing) 2
 
< 0.1%

Length

2025-02-27T05:04:39.862494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-27T05:04:39.947779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
h 39806
68.9%
u 12062
 
20.9%
t 5900
 
10.2%

Most occurring characters

ValueCountFrequency (%)
h 39806
68.9%
u 12062
 
20.9%
t 5900
 
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 39806
68.9%
u 12062
 
20.9%
t 5900
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 39806
68.9%
u 12062
 
20.9%
t 5900
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 39806
68.9%
u 12062
 
20.9%
t 5900
 
10.2%

SellerG
Categorical

High cardinality 

Distinct363
Distinct (%)0.6%
Missing2
Missing (%)< 0.1%
Memory size140.8 KiB
Jellis
5663 
Barry
5321 
Nelson
5288 
hockingstuart
4318 
Marshall
3400 
Other values (358)
33778 

Length

Max length27
Median length22
Mean length6.2819727
Min length1

Characters and Unicode

Total characters362897
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBuxton
2nd rowhockingstuart
3rd rowYPA
4th rowBuxton
5th rowMICM

Common Values

ValueCountFrequency (%)
Jellis 5663
 
9.8%
Barry 5321
 
9.2%
Nelson 5288
 
9.2%
hockingstuart 4318
 
7.5%
Marshall 3400
 
5.9%
Ray 3227
 
5.6%
Buxton 3068
 
5.3%
Biggin 1512
 
2.6%
Fletchers 1400
 
2.4%
Brad 1156
 
2.0%
Other values (353) 23415
40.5%

Length

2025-02-27T05:04:40.080026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jellis 5663
 
9.8%
barry 5321
 
9.2%
nelson 5288
 
9.2%
hockingstuart 4318
 
7.5%
marshall 3400
 
5.9%
ray 3227
 
5.6%
buxton 3068
 
5.3%
biggin 1512
 
2.6%
fletchers 1400
 
2.4%
brad 1156
 
2.0%
Other values (349) 23415
40.5%

Most occurring characters

ValueCountFrequency (%)
l 32464
 
8.9%
a 31601
 
8.7%
r 30341
 
8.4%
s 27475
 
7.6%
e 26111
 
7.2%
o 22203
 
6.1%
n 20436
 
5.6%
i 20005
 
5.5%
t 16643
 
4.6%
B 12309
 
3.4%
Other values (48) 123309
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 362897
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 32464
 
8.9%
a 31601
 
8.7%
r 30341
 
8.4%
s 27475
 
7.6%
e 26111
 
7.2%
o 22203
 
6.1%
n 20436
 
5.6%
i 20005
 
5.5%
t 16643
 
4.6%
B 12309
 
3.4%
Other values (48) 123309
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 362897
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 32464
 
8.9%
a 31601
 
8.7%
r 30341
 
8.4%
s 27475
 
7.6%
e 26111
 
7.2%
o 22203
 
6.1%
n 20436
 
5.6%
i 20005
 
5.5%
t 16643
 
4.6%
B 12309
 
3.4%
Other values (48) 123309
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 362897
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 32464
 
8.9%
a 31601
 
8.7%
r 30341
 
8.4%
s 27475
 
7.6%
e 26111
 
7.2%
o 22203
 
6.1%
n 20436
 
5.6%
i 20005
 
5.5%
t 16643
 
4.6%
B 12309
 
3.4%
Other values (48) 123309
34.0%

Distance
Real number (ℝ)

High correlation 

Distinct213
Distinct (%)0.4%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean11.190472
Minimum0
Maximum48.1
Zeros118
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size451.5 KiB
2025-02-27T05:04:40.276410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.7
Q16.4
median10.3
Q314
95-th percentile24.7
Maximum48.1
Range48.1
Interquartile range (IQR)7.6

Descriptive statistics

Standard deviation6.8051067
Coefficient of variation (CV)0.60811615
Kurtosis3.5869426
Mean11.190472
Median Absolute Deviation (MAD)3.9
Skewness1.5074681
Sum646428.8
Variance46.309477
MonotonicityNot monotonic
2025-02-27T05:04:40.439998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.2 2320
 
4.0%
7.8 1115
 
1.9%
13.8 1114
 
1.9%
9.2 1109
 
1.9%
10.5 1107
 
1.9%
8.4 1008
 
1.7%
4.6 966
 
1.7%
14.7 930
 
1.6%
5.2 929
 
1.6%
11.4 827
 
1.4%
Other values (203) 46341
80.2%
ValueCountFrequency (%)
0 118
 
0.2%
0.7 46
 
0.1%
1.2 76
 
0.1%
1.3 50
 
0.1%
1.4 10
 
< 0.1%
1.5 48
 
0.1%
1.6 328
0.6%
1.8 262
0.5%
1.9 246
0.4%
2 93
 
0.2%
ValueCountFrequency (%)
48.1 8
 
< 0.1%
47.4 12
 
< 0.1%
47.3 31
0.1%
45.9 63
0.1%
45.2 4
 
< 0.1%
44.2 34
0.1%
43.4 2
 
< 0.1%
43.3 10
 
< 0.1%
41 31
0.1%
39.8 4
 
< 0.1%

Landsize
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct1590
Distinct (%)4.2%
Missing19563
Missing (%)33.9%
Infinite0
Infinite (%)0.0%
Mean603.21849
Minimum0
Maximum433014
Zeros4021
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size451.5 KiB
2025-02-27T05:04:40.618220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1225
median523
Q3670
95-th percentile1000
Maximum433014
Range433014
Interquartile range (IQR)445

Descriptive statistics

Standard deviation3689.3089
Coefficient of variation (CV)6.1160408
Kurtosis10059.087
Mean603.21849
Median Absolute Deviation (MAD)210
Skewness90.365639
Sum23047169
Variance13611000
MonotonicityNot monotonic
2025-02-27T05:04:40.797521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4021
 
7.0%
650 324
 
0.6%
697 202
 
0.3%
585 159
 
0.3%
604 135
 
0.2%
696 127
 
0.2%
700 127
 
0.2%
534 123
 
0.2%
530 119
 
0.2%
652 114
 
0.2%
Other values (1580) 32756
56.7%
(Missing) 19563
33.9%
ValueCountFrequency (%)
0 4021
7.0%
1 4
 
< 0.1%
2 2
 
< 0.1%
3 2
 
< 0.1%
5 2
 
< 0.1%
10 2
 
< 0.1%
14 2
 
< 0.1%
15 4
 
< 0.1%
17 3
 
< 0.1%
28 3
 
< 0.1%
ValueCountFrequency (%)
433014 2
< 0.1%
146699 2
< 0.1%
89030 2
< 0.1%
80000 2
< 0.1%
76000 2
< 0.1%
75100 2
< 0.1%
42800 2
< 0.1%
41400 2
< 0.1%
40500 2
< 0.1%
40469 2
< 0.1%

Postcode
Categorical

High cardinality 

Distinct208
Distinct (%)0.4%
Missing4
Missing (%)< 0.1%
Memory size122.7 KiB
3073.0
 
1381
3020.0
 
1059
3046.0
 
1018
3121.0
 
999
3165.0
 
962
Other values (203)
52347 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters346596
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3165.0
2nd row3188.0
3rd row3064.0
4th row3186.0
5th row3000.0

Common Values

ValueCountFrequency (%)
3073.0 1381
 
2.4%
3020.0 1059
 
1.8%
3046.0 1018
 
1.8%
3121.0 999
 
1.7%
3165.0 962
 
1.7%
3040.0 937
 
1.6%
3058.0 908
 
1.6%
3012.0 861
 
1.5%
3204.0 850
 
1.5%
3146.0 838
 
1.5%
Other values (198) 47953
83.0%

Length

2025-02-27T05:04:40.957786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3073.0 1381
 
2.4%
3020.0 1059
 
1.8%
3046.0 1018
 
1.8%
3121.0 999
 
1.7%
3165.0 962
 
1.7%
3040.0 937
 
1.6%
3058.0 908
 
1.6%
3012.0 861
 
1.5%
3204.0 850
 
1.5%
3146.0 838
 
1.5%
Other values (198) 47953
83.0%

Most occurring characters

ValueCountFrequency (%)
0 97758
28.2%
3 70032
20.2%
. 57766
16.7%
1 37155
 
10.7%
2 16399
 
4.7%
4 15900
 
4.6%
8 12087
 
3.5%
6 11487
 
3.3%
7 11144
 
3.2%
5 10875
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 346596
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 97758
28.2%
3 70032
20.2%
. 57766
16.7%
1 37155
 
10.7%
2 16399
 
4.7%
4 15900
 
4.6%
8 12087
 
3.5%
6 11487
 
3.3%
7 11144
 
3.2%
5 10875
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 346596
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 97758
28.2%
3 70032
20.2%
. 57766
16.7%
1 37155
 
10.7%
2 16399
 
4.7%
4 15900
 
4.6%
8 12087
 
3.5%
6 11487
 
3.3%
7 11144
 
3.2%
5 10875
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 346596
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 97758
28.2%
3 70032
20.2%
. 57766
16.7%
1 37155
 
10.7%
2 16399
 
4.7%
4 15900
 
4.6%
8 12087
 
3.5%
6 11487
 
3.3%
7 11144
 
3.2%
5 10875
 
3.1%

Date
Date

Distinct78
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Memory size451.5 KiB
Minimum2016-01-28 00:00:00
Maximum2018-10-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-27T05:04:41.046917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:41.185486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Price
Real number (ℝ)

High correlation  Missing 

Distinct2638
Distinct (%)5.8%
Missing12581
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean1054870.5
Minimum85000
Maximum11200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size451.5 KiB
2025-02-27T05:04:41.323694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum85000
5-th percentile415000
Q1637000
median875000
Q31300000
95-th percentile2250000
Maximum11200000
Range11115000
Interquartile range (IQR)663000

Descriptive statistics

Standard deviation646682.05
Coefficient of variation (CV)0.61304403
Kurtosis13.705671
Mean1054870.5
Median Absolute Deviation (MAD)295000
Skewness2.6286281
Sum4.7668542 × 1010
Variance4.1819768 × 1011
MonotonicityNot monotonic
2025-02-27T05:04:41.502634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600000 412
 
0.7%
650000 383
 
0.7%
1100000 368
 
0.6%
800000 361
 
0.6%
1300000 356
 
0.6%
1000000 352
 
0.6%
1200000 351
 
0.6%
700000 329
 
0.6%
750000 329
 
0.6%
900000 319
 
0.6%
Other values (2628) 41629
72.1%
(Missing) 12581
 
21.8%
ValueCountFrequency (%)
85000 2
 
< 0.1%
121000 2
 
< 0.1%
145000 4
< 0.1%
160000 2
 
< 0.1%
170000 4
< 0.1%
185000 4
< 0.1%
200000 4
< 0.1%
210000 7
< 0.1%
211000 2
 
< 0.1%
215000 2
 
< 0.1%
ValueCountFrequency (%)
11200000 2
< 0.1%
9000000 2
< 0.1%
8000000 2
< 0.1%
7650000 2
< 0.1%
7000000 2
< 0.1%
6800000 2
< 0.1%
6600000 2
< 0.1%
6500000 2
< 0.1%
6460000 2
< 0.1%
6400000 4
< 0.1%

Bathroom
Real number (ℝ)

High correlation  Missing 

Distinct10
Distinct (%)< 0.1%
Missing13627
Missing (%)23.6%
Infinite0
Infinite (%)0.0%
Mean1.6247197
Minimum0
Maximum9
Zeros88
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size507.9 KiB
2025-02-27T05:04:41.638145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.72122438
Coefficient of variation (CV)0.44390697
Kurtosis3.5314865
Mean1.6247197
Median Absolute Deviation (MAD)1
Skewness1.266452
Sum71720
Variance0.5201646
MonotonicityNot monotonic
2025-02-27T05:04:41.741318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 21442
37.1%
2 18399
31.8%
3 3601
 
6.2%
4 455
 
0.8%
5 115
 
0.2%
0 88
 
0.2%
6 29
 
0.1%
7 6
 
< 0.1%
8 6
 
< 0.1%
9 2
 
< 0.1%
(Missing) 13627
23.6%
ValueCountFrequency (%)
0 88
 
0.2%
1 21442
37.1%
2 18399
31.8%
3 3601
 
6.2%
4 455
 
0.8%
5 115
 
0.2%
6 29
 
0.1%
7 6
 
< 0.1%
8 6
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
9 2
 
< 0.1%
8 6
 
< 0.1%
7 6
 
< 0.1%
6 29
 
0.1%
5 115
 
0.2%
4 455
 
0.8%
3 3601
 
6.2%
2 18399
31.8%
1 21442
37.1%
0 88
 
0.2%

Rooms
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.0338769
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size507.9 KiB
2025-02-27T05:04:41.832183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum16
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.97322244
Coefficient of variation (CV)0.32078508
Kurtosis2.9885294
Mean3.0338769
Median Absolute Deviation (MAD)1
Skewness0.53782689
Sum175261
Variance0.94716193
MonotonicityNot monotonic
2025-02-27T05:04:41.936733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 24972
43.2%
2 13738
23.8%
4 13227
22.9%
5 2927
 
5.1%
1 2467
 
4.3%
6 334
 
0.6%
7 45
 
0.1%
8 30
 
0.1%
10 10
 
< 0.1%
12 8
 
< 0.1%
Other values (2) 10
 
< 0.1%
ValueCountFrequency (%)
1 2467
 
4.3%
2 13738
23.8%
3 24972
43.2%
4 13227
22.9%
5 2927
 
5.1%
6 334
 
0.6%
7 45
 
0.1%
8 30
 
0.1%
9 8
 
< 0.1%
10 10
 
< 0.1%
ValueCountFrequency (%)
16 2
 
< 0.1%
12 8
 
< 0.1%
10 10
 
< 0.1%
9 8
 
< 0.1%
8 30
 
0.1%
7 45
 
0.1%
6 334
 
0.6%
5 2927
 
5.1%
4 13227
22.9%
3 24972
43.2%

Regionname
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing6
Missing (%)< 0.1%
Memory size57.4 KiB
Southern Metropolitan
19653 
Northern Metropolitan
15679 
Western Metropolitan
11426 
Eastern Metropolitan
7221 
South-Eastern Metropolitan
2886 
Other values (3)
 
899

Length

Max length26
Median length21
Mean length20.855117
Min length16

Characters and Unicode

Total characters1204675
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouthern Metropolitan
2nd rowSouthern Metropolitan
3rd rowNorthern Metropolitan
4th rowSouthern Metropolitan
5th rowNorthern Metropolitan

Common Values

ValueCountFrequency (%)
Southern Metropolitan 19653
34.0%
Northern Metropolitan 15679
27.1%
Western Metropolitan 11426
19.8%
Eastern Metropolitan 7221
 
12.5%
South-Eastern Metropolitan 2886
 
5.0%
Eastern Victoria 385
 
0.7%
Northern Victoria 343
 
0.6%
Western Victoria 171
 
0.3%
(Missing) 6
 
< 0.1%

Length

2025-02-27T05:04:42.103112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-27T05:04:42.484757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
metropolitan 56865
49.2%
southern 19653
 
17.0%
northern 16022
 
13.9%
western 11597
 
10.0%
eastern 7606
 
6.6%
south-eastern 2886
 
2.5%
victoria 899
 
0.8%

Most occurring characters

ValueCountFrequency (%)
t 175279
14.5%
o 153190
12.7%
r 131550
10.9%
e 126226
10.5%
n 114629
9.5%
a 68256
 
5.7%
i 58663
 
4.9%
57764
 
4.8%
p 56865
 
4.7%
l 56865
 
4.7%
Other values (11) 205388
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1204675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 175279
14.5%
o 153190
12.7%
r 131550
10.9%
e 126226
10.5%
n 114629
9.5%
a 68256
 
5.7%
i 58663
 
4.9%
57764
 
4.8%
p 56865
 
4.7%
l 56865
 
4.7%
Other values (11) 205388
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1204675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 175279
14.5%
o 153190
12.7%
r 131550
10.9%
e 126226
10.5%
n 114629
9.5%
a 68256
 
5.7%
i 58663
 
4.9%
57764
 
4.8%
p 56865
 
4.7%
l 56865
 
4.7%
Other values (11) 205388
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1204675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 175279
14.5%
o 153190
12.7%
r 131550
10.9%
e 126226
10.5%
n 114629
9.5%
a 68256
 
5.7%
i 58663
 
4.9%
57764
 
4.8%
p 56865
 
4.7%
l 56865
 
4.7%
Other values (11) 205388
17.0%

Car
Real number (ℝ)

Missing  Zeros 

Distinct14
Distinct (%)< 0.1%
Missing14477
Missing (%)25.1%
Infinite0
Infinite (%)0.0%
Mean1.7290786
Minimum0
Maximum26
Zeros2765
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size507.9 KiB
2025-02-27T05:04:42.671576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile4
Maximum26
Range26
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0122524
Coefficient of variation (CV)0.58542881
Kurtosis23.07582
Mean1.7290786
Median Absolute Deviation (MAD)1
Skewness2.1366567
Sum74857
Variance1.024655
MonotonicityNot monotonic
2025-02-27T05:04:42.922019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 20190
34.9%
1 15096
26.1%
0 2765
 
4.8%
3 2733
 
4.7%
4 1933
 
3.3%
5 243
 
0.4%
6 234
 
0.4%
7 45
 
0.1%
8 36
 
0.1%
10 10
 
< 0.1%
Other values (4) 8
 
< 0.1%
(Missing) 14477
25.1%
ValueCountFrequency (%)
0 2765
 
4.8%
1 15096
26.1%
2 20190
34.9%
3 2733
 
4.7%
4 1933
 
3.3%
5 243
 
0.4%
6 234
 
0.4%
7 45
 
0.1%
8 36
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
26 2
 
< 0.1%
18 2
 
< 0.1%
11 2
 
< 0.1%
10 10
 
< 0.1%
9 2
 
< 0.1%
8 36
 
0.1%
7 45
 
0.1%
6 234
 
0.4%
5 243
 
0.4%
4 1933
3.3%

CouncilArea
Categorical

High correlation 

Distinct33
Distinct (%)0.1%
Missing6
Missing (%)< 0.1%
Memory size59.9 KiB
Boroondara City Council
6192 
Darebin City Council
4637 
Moreland City Council
 
3481
Glen Eira City Council
 
3279
Melbourne City Council
 
3226
Other values (28)
36949 

Length

Max length30
Median length26
Mean length21.746486
Min length17

Characters and Unicode

Total characters1256164
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGlen Eira City Council
2nd rowBayside City Council
3rd rowHume City Council
4th rowBayside City Council
5th rowMelbourne City Council

Common Values

ValueCountFrequency (%)
Boroondara City Council 6192
 
10.7%
Darebin City Council 4637
 
8.0%
Moreland City Council 3481
 
6.0%
Glen Eira City Council 3279
 
5.7%
Melbourne City Council 3226
 
5.6%
Banyule City Council 3087
 
5.3%
Moonee Valley City Council 2977
 
5.2%
Bayside City Council 2938
 
5.1%
Brimbank City Council 2686
 
4.6%
Maribyrnong City Council 2463
 
4.3%
Other values (23) 22798
39.5%

Length

2025-02-27T05:04:43.191564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
council 57764
31.4%
city 57275
31.1%
boroondara 6192
 
3.4%
darebin 4637
 
2.5%
moreland 3481
 
1.9%
glen 3279
 
1.8%
eira 3279
 
1.8%
melbourne 3226
 
1.8%
banyule 3087
 
1.7%
moonee 2977
 
1.6%
Other values (31) 38841
21.1%

Most occurring characters

ValueCountFrequency (%)
i 144208
11.5%
126274
10.1%
n 119868
9.5%
C 115396
 
9.2%
o 110273
 
8.8%
l 83231
 
6.6%
y 71652
 
5.7%
t 70952
 
5.6%
u 66226
 
5.3%
c 57881
 
4.6%
Other values (27) 290203
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1256164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 144208
11.5%
126274
10.1%
n 119868
9.5%
C 115396
 
9.2%
o 110273
 
8.8%
l 83231
 
6.6%
y 71652
 
5.7%
t 70952
 
5.6%
u 66226
 
5.3%
c 57881
 
4.6%
Other values (27) 290203
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1256164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 144208
11.5%
126274
10.1%
n 119868
9.5%
C 115396
 
9.2%
o 110273
 
8.8%
l 83231
 
6.6%
y 71652
 
5.7%
t 70952
 
5.6%
u 66226
 
5.3%
c 57881
 
4.6%
Other values (27) 290203
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1256164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 144208
11.5%
126274
10.1%
n 119868
9.5%
C 115396
 
9.2%
o 110273
 
8.8%
l 83231
 
6.6%
y 71652
 
5.7%
t 70952
 
5.6%
u 66226
 
5.3%
c 57881
 
4.6%
Other values (27) 290203
23.1%

Address
Categorical

High cardinality  Uniform 

Distinct27304
Distinct (%)47.3%
Missing2
Missing (%)< 0.1%
Memory size2.7 MiB
5 Charles St
 
8
9 Margaret St
 
7
13 Robinson St
 
7
39 Moore St
 
7
57 Bay Rd
 
7
Other values (27299)
57732 

Length

Max length27
Median length24
Mean length13.559497
Min length8

Characters and Unicode

Total characters783305
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row794 North Rd
2nd row10 Fewster Rd
3rd row11 Abercarn Av
4th row6/105 Cochrane St
5th row608/547 Flinders La

Common Values

ValueCountFrequency (%)
5 Charles St 8
 
< 0.1%
9 Margaret St 7
 
< 0.1%
13 Robinson St 7
 
< 0.1%
39 Moore St 7
 
< 0.1%
57 Bay Rd 7
 
< 0.1%
1088 Toorak Rd 7
 
< 0.1%
14 Rose St 6
 
< 0.1%
12 Grandview Av 6
 
< 0.1%
33 McCracken St 6
 
< 0.1%
53 William St 6
 
< 0.1%
Other values (27294) 57701
99.9%

Length

2025-02-27T05:04:43.355686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st 28660
 
16.5%
rd 10811
 
6.2%
av 5594
 
3.2%
ct 2948
 
1.7%
dr 2051
 
1.2%
cr 1967
 
1.1%
gr 1201
 
0.7%
3 1185
 
0.7%
4 1119
 
0.6%
5 1057
 
0.6%
Other values (11442) 117523
67.5%

Most occurring characters

ValueCountFrequency (%)
116348
 
14.9%
t 49342
 
6.3%
e 41329
 
5.3%
r 37904
 
4.8%
a 36615
 
4.7%
S 32742
 
4.2%
n 31500
 
4.0%
1 29981
 
3.8%
o 28158
 
3.6%
l 27700
 
3.5%
Other values (54) 351686
44.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 783305
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
116348
 
14.9%
t 49342
 
6.3%
e 41329
 
5.3%
r 37904
 
4.8%
a 36615
 
4.7%
S 32742
 
4.2%
n 31500
 
4.0%
1 29981
 
3.8%
o 28158
 
3.6%
l 27700
 
3.5%
Other values (54) 351686
44.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 783305
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
116348
 
14.9%
t 49342
 
6.3%
e 41329
 
5.3%
r 37904
 
4.8%
a 36615
 
4.7%
S 32742
 
4.2%
n 31500
 
4.0%
1 29981
 
3.8%
o 28158
 
3.6%
l 27700
 
3.5%
Other values (54) 351686
44.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 783305
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
116348
 
14.9%
t 49342
 
6.3%
e 41329
 
5.3%
r 37904
 
4.8%
a 36615
 
4.7%
S 32742
 
4.2%
n 31500
 
4.0%
1 29981
 
3.8%
o 28158
 
3.6%
l 27700
 
3.5%
Other values (54) 351686
44.9%

Method
Categorical

Distinct9
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size57.3 KiB
S
32690 
SP
8434 
PI
8057 
VB
5181 
SN
 
2231
Other values (4)
 
1175

Length

Max length2
Median length1
Mean length1.4295804
Min length1

Characters and Unicode

Total characters82584
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowSP
5th rowS

Common Values

ValueCountFrequency (%)
S 32690
56.6%
SP 8434
 
14.6%
PI 8057
 
13.9%
VB 5181
 
9.0%
SN 2231
 
3.9%
PN 489
 
0.8%
SA 364
 
0.6%
W 262
 
0.5%
SS 60
 
0.1%
(Missing) 2
 
< 0.1%

Length

2025-02-27T05:04:43.486595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-27T05:04:43.593844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
s 32690
56.6%
sp 8434
 
14.6%
pi 8057
 
13.9%
vb 5181
 
9.0%
sn 2231
 
3.9%
pn 489
 
0.8%
sa 364
 
0.6%
w 262
 
0.5%
ss 60
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 43839
53.1%
P 16980
 
20.6%
I 8057
 
9.8%
V 5181
 
6.3%
B 5181
 
6.3%
N 2720
 
3.3%
A 364
 
0.4%
W 262
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 82584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 43839
53.1%
P 16980
 
20.6%
I 8057
 
9.8%
V 5181
 
6.3%
B 5181
 
6.3%
N 2720
 
3.3%
A 364
 
0.4%
W 262
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 82584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 43839
53.1%
P 16980
 
20.6%
I 8057
 
9.8%
V 5181
 
6.3%
B 5181
 
6.3%
N 2720
 
3.3%
A 364
 
0.4%
W 262
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 82584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 43839
53.1%
P 16980
 
20.6%
I 8057
 
9.8%
V 5181
 
6.3%
B 5181
 
6.3%
N 2720
 
3.3%
A 364
 
0.4%
W 262
 
0.3%

Propertycount
Real number (ℝ)

Distinct338
Distinct (%)0.6%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7576.2494
Minimum83
Maximum21650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size507.9 KiB
2025-02-27T05:04:43.705765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum83
5-th percentile2185
Q14385
median6763
Q310412
95-th percentile15510
Maximum21650
Range21567
Interquartile range (IQR)6027

Descriptive statistics

Standard deviation4420.2191
Coefficient of variation (CV)0.58343104
Kurtosis0.88656299
Mean7576.2494
Median Absolute Deviation (MAD)2775
Skewness0.98918052
Sum4.3763447 × 108
Variance19538337
MonotonicityNot monotonic
2025-02-27T05:04:43.809430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21650 1381
 
2.4%
8870 1156
 
2.0%
10969 962
 
1.7%
14949 902
 
1.6%
10412 838
 
1.5%
10331 777
 
1.3%
10579 764
 
1.3%
14577 759
 
1.3%
11918 734
 
1.3%
8920 730
 
1.3%
Other values (328) 48761
84.4%
ValueCountFrequency (%)
83 2
 
< 0.1%
121 2
 
< 0.1%
129 2
 
< 0.1%
242 2
 
< 0.1%
249 8
 
< 0.1%
290 4
 
< 0.1%
335 3
 
< 0.1%
389 21
< 0.1%
394 30
0.1%
438 20
< 0.1%
ValueCountFrequency (%)
21650 1381
2.4%
17496 351
 
0.6%
17384 31
 
0.1%
17093 90
 
0.2%
17055 208
 
0.4%
16166 295
 
0.5%
15542 213
 
0.4%
15510 410
 
0.7%
15321 397
 
0.7%
14949 902
1.6%

Bedroom2
Real number (ℝ)

High correlation  Missing 

Distinct14
Distinct (%)< 0.1%
Missing13615
Missing (%)23.6%
Infinite0
Infinite (%)0.0%
Mean3.0851319
Minimum0
Maximum20
Zeros32
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size507.9 KiB
2025-02-27T05:04:43.889239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum20
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.96911808
Coefficient of variation (CV)0.31412533
Kurtosis7.021931
Mean3.0851319
Median Absolute Deviation (MAD)1
Skewness0.71334238
Sum136224
Variance0.93918986
MonotonicityNot monotonic
2025-02-27T05:04:43.964678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 19704
34.1%
4 10524
18.2%
2 9515
16.5%
5 2411
 
4.2%
1 1616
 
2.8%
6 269
 
0.5%
7 43
 
0.1%
0 32
 
0.1%
8 22
 
< 0.1%
10 8
 
< 0.1%
Other values (4) 11
 
< 0.1%
(Missing) 13615
23.6%
ValueCountFrequency (%)
0 32
 
0.1%
1 1616
 
2.8%
2 9515
16.5%
3 19704
34.1%
4 10524
18.2%
5 2411
 
4.2%
6 269
 
0.5%
7 43
 
0.1%
8 22
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
20 2
 
< 0.1%
16 2
 
< 0.1%
12 3
 
< 0.1%
10 8
 
< 0.1%
9 4
 
< 0.1%
8 22
 
< 0.1%
7 43
 
0.1%
6 269
 
0.5%
5 2411
 
4.2%
4 10524
18.2%

Lattitude
Real number (ℝ)

High correlation  Missing 

Distinct11505
Distinct (%)25.8%
Missing13208
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean-37.810628
Minimum-38.19043
Maximum-37.3902
Zeros0
Zeros (%)0.0%
Negative44562
Negative (%)77.1%
Memory size451.5 KiB
2025-02-27T05:04:44.056700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-38.19043
5-th percentile-37.948909
Q1-37.86268
median-37.80772
Q3-37.7547
95-th percentile-37.674342
Maximum-37.3902
Range0.80023
Interquartile range (IQR)0.10798

Descriptive statistics

Standard deviation0.090463936
Coefficient of variation (CV)-0.0023925531
Kurtosis1.5900214
Mean-37.810628
Median Absolute Deviation (MAD)0.0539
Skewness-0.24674112
Sum-1684917.2
Variance0.0081837237
MonotonicityNot monotonic
2025-02-27T05:04:44.168870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-37.8361 44
 
0.1%
-37.8424 38
 
0.1%
-37.7956 31
 
0.1%
-37.8198 31
 
0.1%
-37.847 30
 
0.1%
-37.8536 29
 
0.1%
-37.8167 29
 
0.1%
-37.8127 29
 
0.1%
-37.8415 28
 
< 0.1%
-37.8117 27
 
< 0.1%
Other values (11495) 44246
76.6%
(Missing) 13208
 
22.9%
ValueCountFrequency (%)
-38.19043 2
< 0.1%
-38.1856 2
< 0.1%
-38.18463 2
< 0.1%
-38.18418 2
< 0.1%
-38.18255 2
< 0.1%
-38.18163 3
< 0.1%
-38.17928 2
< 0.1%
-38.17829 2
< 0.1%
-38.17745 3
< 0.1%
-38.17436 2
< 0.1%
ValueCountFrequency (%)
-37.3902 2
< 0.1%
-37.3951 2
< 0.1%
-37.3978 2
< 0.1%
-37.39946 2
< 0.1%
-37.4072 2
< 0.1%
-37.40744 2
< 0.1%
-37.40758 2
< 0.1%
-37.40853 2
< 0.1%
-37.4128 2
< 0.1%
-37.41318 2
< 0.1%

YearBuilt
Real number (ℝ)

Missing 

Distinct157
Distinct (%)0.6%
Missing31927
Missing (%)55.3%
Infinite0
Infinite (%)0.0%
Mean1965.039
Minimum1196
Maximum2106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size507.9 KiB
2025-02-27T05:04:44.275452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1196
5-th percentile1900
Q11940
median1970
Q32000
95-th percentile2013
Maximum2106
Range910
Interquartile range (IQR)60

Descriptive statistics

Standard deviation37.652234
Coefficient of variation (CV)0.019161062
Kurtosis12.69246
Mean1965.039
Median Absolute Deviation (MAD)30
Skewness-1.1559976
Sum50782502
Variance1417.6907
MonotonicityNot monotonic
2025-02-27T05:04:44.405331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1970 2399
 
4.2%
1960 2088
 
3.6%
1950 1820
 
3.2%
1980 1178
 
2.0%
1900 985
 
1.7%
2000 962
 
1.7%
1920 922
 
1.6%
1930 910
 
1.6%
1910 816
 
1.4%
1890 761
 
1.3%
Other values (147) 13002
22.5%
(Missing) 31927
55.3%
ValueCountFrequency (%)
1196 2
 
< 0.1%
1820 2
 
< 0.1%
1830 2
 
< 0.1%
1850 6
 
< 0.1%
1854 4
 
< 0.1%
1855 2
 
< 0.1%
1856 2
 
< 0.1%
1857 2
 
< 0.1%
1860 18
< 0.1%
1862 2
 
< 0.1%
ValueCountFrequency (%)
2106 2
 
< 0.1%
2019 2
 
< 0.1%
2018 8
 
< 0.1%
2017 131
 
0.2%
2016 208
 
0.4%
2015 260
0.5%
2014 360
0.6%
2013 415
0.7%
2012 541
0.9%
2011 405
0.7%

Suburb
Categorical

High cardinality 

Distinct347
Distinct (%)0.6%
Missing2
Missing (%)< 0.1%
Memory size141.1 KiB
Reservoir
 
1381
Bentleigh East
 
962
Richmond
 
902
Glen Iris
 
838
Kew
 
777
Other values (342)
52908 

Length

Max length18
Median length15
Mean length9.8191386
Min length3

Characters and Unicode

Total characters567232
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBentleigh East
2nd rowHampton
3rd rowCraigieburn
4th rowBrighton
5th rowMelbourne

Common Values

ValueCountFrequency (%)
Reservoir 1381
 
2.4%
Bentleigh East 962
 
1.7%
Richmond 902
 
1.6%
Glen Iris 838
 
1.5%
Kew 777
 
1.3%
Brighton 764
 
1.3%
Preston 759
 
1.3%
Brunswick 734
 
1.3%
Camberwell 732
 
1.3%
Hawthorn 727
 
1.3%
Other values (337) 49192
85.2%

Length

2025-02-27T05:04:44.549456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
east 4523
 
5.7%
north 3025
 
3.8%
south 2276
 
2.9%
west 1832
 
2.3%
melbourne 1745
 
2.2%
bentleigh 1481
 
1.9%
park 1476
 
1.9%
brunswick 1451
 
1.8%
brighton 1406
 
1.8%
reservoir 1381
 
1.7%
Other values (293) 59132
74.2%

Most occurring characters

ValueCountFrequency (%)
e 50902
 
9.0%
r 48025
 
8.5%
o 47720
 
8.4%
n 41387
 
7.3%
a 39027
 
6.9%
t 34390
 
6.1%
l 31745
 
5.6%
i 26185
 
4.6%
s 25790
 
4.5%
21960
 
3.9%
Other values (39) 200101
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 50902
 
9.0%
r 48025
 
8.5%
o 47720
 
8.4%
n 41387
 
7.3%
a 39027
 
6.9%
t 34390
 
6.1%
l 31745
 
5.6%
i 26185
 
4.6%
s 25790
 
4.5%
21960
 
3.9%
Other values (39) 200101
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 50902
 
9.0%
r 48025
 
8.5%
o 47720
 
8.4%
n 41387
 
7.3%
a 39027
 
6.9%
t 34390
 
6.1%
l 31745
 
5.6%
i 26185
 
4.6%
s 25790
 
4.5%
21960
 
3.9%
Other values (39) 200101
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 50902
 
9.0%
r 48025
 
8.5%
o 47720
 
8.4%
n 41387
 
7.3%
a 39027
 
6.9%
t 34390
 
6.1%
l 31745
 
5.6%
i 26185
 
4.6%
s 25790
 
4.5%
21960
 
3.9%
Other values (39) 200101
35.3%

Interactions

2025-02-27T05:04:36.018576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-27T05:04:35.568977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:37.130748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:19.990170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:21.407118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:22.774182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:24.430758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:25.846107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:27.440216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:29.374066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:31.044014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:32.532260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:33.914915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:35.706694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:37.246320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:20.144587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:21.533525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:22.896661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:24.550586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:26.020844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:27.607626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:29.508970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:31.173236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:32.630942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:34.037908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-27T05:04:35.841080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-27T05:04:44.644987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BathroomBedroom2BuildingAreaCarCouncilAreaDistanceLandsizeLattitudeLongtitudeMethodPricePropertycountRegionnameRoomsTypeYearBuilt
Bathroom1.0000.6120.6710.3690.1080.1710.218-0.0690.1450.0400.374-0.0150.0770.6160.2110.221
Bedroom20.6121.0000.7520.4720.1580.3330.4720.0090.1610.0320.453-0.0410.1000.9710.4200.004
BuildingArea0.6710.7521.0000.4660.0440.2470.455-0.0480.1520.0000.553-0.0420.0220.7630.0230.075
Car0.3690.4720.4661.0000.0810.3180.408-0.0130.1240.0290.246-0.0210.0340.4680.1250.132
CouncilArea0.1080.1580.0440.0811.0000.6850.1040.7390.7650.0820.1750.4200.8500.1530.2570.191
Distance0.1710.3330.2470.3180.6851.0000.430-0.0260.3190.044-0.190-0.0860.4330.3310.1990.331
Landsize0.2180.4720.4550.4080.1040.4301.0000.0220.2500.0090.271-0.0450.0530.4740.000-0.072
Lattitude-0.0690.009-0.048-0.0130.739-0.0260.0221.000-0.3590.043-0.283-0.0490.4550.0080.1390.098
Longtitude0.1450.1610.1520.1240.7650.3190.250-0.3591.0000.0470.2720.0460.5820.1570.142-0.009
Method0.0400.0320.0000.0290.0820.0440.0090.0430.0471.0000.0620.0320.0550.0340.0640.029
Price0.3740.4530.5530.2460.175-0.1900.271-0.2830.2720.0621.000-0.0420.1510.5070.231-0.381
Propertycount-0.015-0.041-0.042-0.0210.420-0.086-0.045-0.0490.0460.032-0.0421.0000.202-0.0630.1410.009
Regionname0.0770.1000.0220.0340.8500.4330.0530.4550.5820.0550.1510.2021.0000.0890.1360.104
Rooms0.6160.9710.7630.4680.1530.3310.4740.0080.1570.0340.507-0.0630.0891.0000.438-0.009
Type0.2110.4200.0230.1250.2570.1990.0000.1390.1420.0640.2310.1410.1360.4381.0000.180
YearBuilt0.2210.0040.0750.1320.1910.331-0.0720.098-0.0090.029-0.3810.0090.104-0.0090.1801.000

Missing values

2025-02-27T05:04:37.465277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-27T05:04:37.769066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-27T05:04:38.646651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

LongtitudeBuildingAreaTypeSellerGDistanceLandsizePostcodeDatePriceBathroomRoomsRegionnameCarCouncilAreaAddressMethodPropertycountBedroom2LattitudeYearBuiltSuburb
0145.05510NaNhBuxton13.8648.03165.02017-09-161520000.024Southern Metropolitan2Glen Eira City Council794 North RdS109694-37.90609<NA>Bentleigh East
1145.01357180.0hhockingstuart13.8662.03188.02017-10-282025000.024Southern Metropolitan2Bayside City Council10 Fewster RdS54544-37.938001920Hampton
2144.93683110.0hYPA20.6641.03064.02017-09-09595000.014Northern Metropolitan0Hume City Council11 Abercarn AvS155104-37.611141990Craigieburn
3144.99881110.0uBuxton10.5100.03186.02017-07-29845000.012Southern Metropolitan1Bayside City Council6/105 Cochrane StSP105792-37.895691960Brighton
4NaNNaNuMICM2.8NaN3000.02016-08-10370000.0<NA>2Northern Metropolitan<NA>Melbourne City Council608/547 Flinders LaS17496<NA>NaN<NA>Melbourne
5NaNNaNhRaine20.6NaN3064.02017-05-27700000.0<NA>4Northern Metropolitan<NA>Hume City Council3 Gaussberg WkS5833<NA>NaN<NA>Roxburgh Park
6144.64003170.0hYPA14.7533.03030.02017-07-29482500.024Western Metropolitan0Wyndham City Council46 Coventry DrS161664-37.893371980Werribee
7145.07505NaNhJellis7.3802.03146.02018-06-01NaN13Southern Metropolitan3Boroondara City Council114 Summerhill RdS104123-37.85902<NA>Glen Iris
8145.12728128.0hRay16.2382.03094.02017-01-07655000.023Eastern Metropolitan3Banyule City Council349 Main RdS38913-37.728571970Montmorency
9145.05250NaNhNelson7.5378.03102.02016-10-151300000.013Southern Metropolitan2Boroondara City Council33a Westbrook StS26713-37.79560<NA>Kew East
LongtitudeBuildingAreaTypeSellerGDistanceLandsizePostcodeDatePriceBathroomRoomsRegionnameCarCouncilAreaAddressMethodPropertycountBedroom2LattitudeYearBuiltSuburb
57760145.06623NaNhMarshall8.4868.03126.02017-09-09NaN25Southern Metropolitan2Boroondara City Council9 Rubens GrS32655-37.82094<NA>Canterbury
57761144.8961075.0uSweeney6.40.03011.02016-06-18370000.012Western Metropolitan1Maribyrnong City Council405/250 Barkly StSP75702-37.799202007Footscray
57762144.9728643.0uMcGrath4.0NaN3057.02017-08-19360000.011Northern Metropolitan0Moreland City Council301/455 Lygon StSP55331-37.763892014Brunswick East
57763NaNNaNtBarry12.4NaN3108.02017-07-151220000.0<NA>4Eastern Metropolitan<NA>Manningham City Council3/8 Persimmon CtPI9028<NA>NaN<NA>Doncaster
57764145.06340NaNhWoodards13.9710.03165.02016-10-151085000.023Southern Metropolitan4Glen Eira City Council14 Vasey StS109693-37.93480<NA>Bentleigh East
57765NaNNaNhMarshall9.2NaN3104.02016-12-112190000.0<NA>5Southern Metropolitan<NA>Boroondara City Council53 Trentwood AvS7809<NA>NaN<NA>Balwyn North
57766145.33967391.0hEview35.2603.03806.02017-08-07950000.044Eastern Victoria2Casey City Council9 Sunview PlSA170934-38.067882007Berwick
57767145.0609096.0uMiles11.4151.03084.02016-11-27618000.012Eastern Metropolitan1Banyule City Council4/32 Lower Plenty RdS35402-37.745801980Rosanna
57768144.9925084.0hFletchers3.3176.03141.02016-09-24NaN12Southern Metropolitan0Melbourne City Council6 Cliff StS148872-37.843601940South Yarra
57769NaNNaNuJellis2.7NaN3141.02017-11-25750000.0<NA>3Southern Metropolitan<NA>Melbourne City Council9/7 Clowes StPI14887<NA>NaN<NA>South Yarra

Duplicate rows

Most frequently occurring

LongtitudeBuildingAreaTypeSellerGDistanceLandsizePostcodeDatePriceBathroomRoomsRegionnameCarCouncilAreaAddressMethodPropertycountBedroom2LattitudeYearBuiltSuburb# duplicates
20099145.16777226.0tJellis15.4405.03131.02017-06-17NaN33Eastern Metropolitan2Manningham City Council1/7 Lilian StSP49733-37.826782000Nunawading4
12144.54532189.0hReliance29.8576.03338.02017-07-29456000.024Western Victoria2Melton City Council30 Pinrush RdS31224-37.691042010Brookfield3
17144.55106119.0hReliance31.7740.03337.02017-05-27410000.023Northern Victoria2Melton City Council3 Becker ClS60653-37.680521985Melton West3
18144.55200149.0hhockingstuart31.7NaN3337.02017-07-10469500.023Northern Victoria2Melton City Council21 Trethowan AvS60653-37.684671990Melton West3
24144.55666115.0hhockingstuart31.7524.03337.02017-09-23439000.023Northern Victoria2Melton City Council24 Marlo DrS60653-37.66869<NA>Melton West3
25144.55682172.0hhockingstuart31.7NaN3337.02017-09-16486000.014Northern Victoria4Melton City Council12 Gloucester WyS60654-37.673581980Melton West3
59144.57211NaNhhockingstuart29.8603.03338.02017-06-17419000.013Western Victoria3Melton City Council24 Brennan StS47183-37.70478<NA>Melton South3
74144.57419124.0hFN29.8NaN3338.02018-06-01361000.013Western Victoria1Melton City Council14 Thomas AvSP47183-37.693821970Melton South3
78144.57645105.0hYPA29.8649.03338.02017-08-26395000.013Western Victoria1Melton City Council1 Fraser StS47183-37.698911975Melton South3
85144.57760223.0hPRDNationwide31.7960.03337.02017-11-11710000.0512Western Victoria3Melton City Council213 Station RdS360012-37.688301970Melton3